
def a__       (			A_ = 100   ):






  '''simple docstring'''





  __magic_name__								=			set()

  __magic_name__								=			0

  __magic_name__								=			n + 1  # maximum limit

  for a in range(2,	A_   ):
    for b in range(2,	A_   ):
      __magic_name__								=			a**b  # calculates the current power
      collect_powers.add(A_   )  # adds the result to the set
  return len(A_   )


if __name__ == "__main__":
    print('Number of terms ', solution(int(str(input()).strip())))



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase     :	List[str]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[Any]       			=  OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__lowerCAmelCase     :	Optional[Any]       			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def a__       (			A_   ):






  '''simple docstring'''





  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      __magic_name__								=			model_type_to_module_name(A_   )

      __magic_name__								=			importlib.import_module(f'''.{module_name}''',	"""transformers.models"""   )
      try:
        return getattr(A_,	A_   )
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(A_,	"""__name__""",	A_   ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  __magic_name__								=			importlib.import_module("""transformers"""   )
  if hasattr(A_,	A_   ):
    return getattr(A_,	A_   )

  return None

def a__       (			A_,	A_ = None,	A_ = False,	A_ = False,	A_ = None,	A_ = None,	A_ = None,	A_ = False,	**A_,	):






  '''simple docstring'''





  __magic_name__								=			get_file_from_repo(
      A_,	A_,	cache_dir=A_,	force_download=A_,	resume_download=A_,	proxies=A_,	use_auth_token=A_,	revision=A_,	local_files_only=A_,	)
  if resolved_config_file is None:
    logger.info(
        """Could not locate the feature extractor configuration file, will try to use the model config instead."""   )
    return {}

  with open(A_,	encoding="""utf-8"""   ) as reader:
    return json.load(A_   )





class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    raise EnvironmentError(
        """AutoFeatureExtractor is designed to be instantiated """
        """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""       )
  @classmethod
  @replace_list_option_in_docstrings(UpperCamelCase__       )
  def 				_lowercase				(					cls     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Optional[int]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""config"""     ,				UpperCamelCase__       )
    __magic_name__								=			kwargs.pop("""trust_remote_code"""     ,				UpperCamelCase__       )
    __magic_name__								=			True

    __magic_name__	,       __magic_name__								=			FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    __magic_name__								=			config_dict.get("""feature_extractor_type"""     ,				UpperCamelCase__       )
    __magic_name__								=			None
    if "AutoFeatureExtractor" in config_dict.get("""auto_map"""     ,				{}       ):
      __magic_name__								=			config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

    # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
    if feature_extractor_class is None and feature_extractor_auto_map is None:
      if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			AutoConfig.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__       )
      # It could be in `config.feature_extractor_type``
      __magic_name__								=			getattr(UpperCamelCase__     ,				"""feature_extractor_type"""     ,				UpperCamelCase__       )
      if hasattr(UpperCamelCase__     ,				"""auto_map"""       ) and "AutoFeatureExtractor" in config.auto_map:
        __magic_name__								=			config.auto_map["""AutoFeatureExtractor"""]

    if feature_extractor_class is not None:
      __magic_name__								=			feature_extractor_class_from_name(UpperCamelCase__       )

    __magic_name__								=			feature_extractor_auto_map is not None
    __magic_name__								=			feature_extractor_class is not None or type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING
    __magic_name__								=			resolve_trust_remote_code(
        UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

    if has_remote_code and trust_remote_code:
      __magic_name__								=			get_class_from_dynamic_module(
          UpperCamelCase__     ,				UpperCamelCase__     ,				**UpperCamelCase__       )
      __magic_name__								=			kwargs.pop("""code_revision"""     ,				UpperCamelCase__       )
      if os.path.isdir(UpperCamelCase__       ):
        feature_extractor_class.register_for_auto_class()
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    elif feature_extractor_class is not None:
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )
    # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
    elif type(UpperCamelCase__       ) in FEATURE_EXTRACTOR_MAPPING:
      __magic_name__								=			FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase__       )]
      return feature_extractor_class.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )

    raise ValueError(
        F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
        F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
        F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()       )}'''       )





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase__     ,				UpperCamelCase__       )

